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pivot points option in UI

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Dobromir Popov
2025-08-13 02:24:12 +03:00
parent 9c1ba6dbe2
commit 6ef1a63054
4 changed files with 124 additions and 106 deletions
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66
core/orchestrator.py
View File

@@ -584,7 +584,6 @@ class TradingOrchestrator:
return alias_to_canonical.get(name, name) return alias_to_canonical.get(name, name)
except Exception: except Exception:
return name return name
def _initialize_ml_models(self): def _initialize_ml_models(self):
"""Initialize ML models for enhanced trading""" """Initialize ML models for enhanced trading"""
try: try:
@@ -738,45 +737,42 @@ class TradingOrchestrator:
checkpoint_metadata = db_manager.get_best_checkpoint_metadata( checkpoint_metadata = db_manager.get_best_checkpoint_metadata(
"enhanced_cnn" "enhanced_cnn"
) )
if checkpoint_metadata: if checkpoint_metadata and os.path.exists(checkpoint_metadata.file_path):
self.model_states["cnn"]["initial_loss"] = 0.412 try:
self.model_states["cnn"]["current_loss"] = ( saved = torch.load(checkpoint_metadata.file_path, map_location=self.device)
checkpoint_metadata.performance_metrics.get("loss", 0.0187) if saved and saved.get("model_state_dict"):
) self.cnn_model.load_state_dict(saved["model_state_dict"], strict=False)
self.model_states["cnn"]["best_loss"] = (
checkpoint_metadata.performance_metrics.get("loss", 0.0134)
)
self.model_states["cnn"]["checkpoint_loaded"] = True
self.model_states["cnn"][
"checkpoint_filename"
] = checkpoint_metadata.checkpoint_id
checkpoint_loaded = True checkpoint_loaded = True
loss_str = f"{checkpoint_metadata.performance_metrics.get('loss', 0.0):.4f}" except Exception as load_ex:
logger.info( logger.warning(f"CNN checkpoint load_state_dict failed: {load_ex}")
f"CNN checkpoint loaded: {checkpoint_metadata.checkpoint_id} (loss={loss_str})" if not checkpoint_loaded:
) # Filesystem fallback
from utils.checkpoint_manager import load_best_checkpoint as _load_best_ckpt
result = _load_best_ckpt("enhanced_cnn")
if result:
ckpt_path, meta = result
try:
saved = torch.load(ckpt_path, map_location=self.device)
if saved and saved.get("model_state_dict"):
self.cnn_model.load_state_dict(saved["model_state_dict"], strict=False)
checkpoint_loaded = True
self.model_states["cnn"]["checkpoint_filename"] = getattr(meta, "checkpoint_id", os.path.basename(ckpt_path))
except Exception as e_load:
logger.warning(f"Failed loading CNN weights from {ckpt_path}: {e_load}")
# Update model_states flags after attempts
self.model_states["cnn"]["checkpoint_loaded"] = checkpoint_loaded
except Exception as e: except Exception as e:
logger.warning(f"Error loading CNN checkpoint: {e}") logger.warning(f"Error loading CNN checkpoint: {e}")
# Filesystem fallback checkpoint_loaded = False
try:
from utils.checkpoint_manager import get_checkpoint_manager
cm = get_checkpoint_manager()
result = cm.load_best_checkpoint("enhanced_cnn")
if result:
model_path, meta = result
self.model_states["cnn"]["checkpoint_loaded"] = True
self.model_states["cnn"]["checkpoint_filename"] = getattr(meta, 'checkpoint_id', None)
checkpoint_loaded = True
logger.info(f"CNN checkpoint (fs) detected: {getattr(meta, 'checkpoint_id', 'unknown')}")
except Exception:
pass
if not checkpoint_loaded: if not checkpoint_loaded:
# New model - no synthetic data # New model - no synthetic data
self.model_states["cnn"]["initial_loss"] = None self.model_states["cnn"]["initial_loss"] = None
self.model_states["cnn"]["current_loss"] = None self.model_states["cnn"]["current_loss"] = None
self.model_states["cnn"]["best_loss"] = None self.model_states["cnn"]["best_loss"] = None
logger.info("CNN starting fresh - no checkpoint found") self.model_states["cnn"]["checkpoint_loaded"] = False
logger.info("CNN starting fresh - no checkpoint found or failed to load")
else:
logger.info("CNN weights loaded from checkpoint successfully")
logger.info("Enhanced CNN model initialized directly") logger.info("Enhanced CNN model initialized directly")
except ImportError: except ImportError:
@@ -1339,7 +1335,6 @@ class TradingOrchestrator:
} }
return stats return stats
def clear_session_data(self): def clear_session_data(self):
"""Clear all session-related data for fresh start""" """Clear all session-related data for fresh start"""
try: try:
@@ -2122,7 +2117,6 @@ class TradingOrchestrator:
except Exception as e: except Exception as e:
logger.error(f"Error registering model {model.name}: {e}") logger.error(f"Error registering model {model.name}: {e}")
return False return False
def unregister_model(self, model_name: str) -> bool: def unregister_model(self, model_name: str) -> bool:
"""Unregister a model""" """Unregister a model"""
try: try:
@@ -3540,7 +3534,6 @@ class TradingOrchestrator:
except Exception as e: except Exception as e:
logger.error(f"Error in immediate training for {model_name}: {e}") logger.error(f"Error in immediate training for {model_name}: {e}")
async def _evaluate_and_train_on_record(self, record: Dict, current_price: float): async def _evaluate_and_train_on_record(self, record: Dict, current_price: float):
"""Evaluate prediction outcome and train model""" """Evaluate prediction outcome and train model"""
try: try:
@@ -5779,7 +5772,6 @@ class TradingOrchestrator:
if symbol in self.recent_decisions: if symbol in self.recent_decisions:
return self.recent_decisions[symbol][-limit:] return self.recent_decisions[symbol][-limit:]
return [] return []
def get_performance_metrics(self) -> Dict[str, Any]: def get_performance_metrics(self) -> Dict[str, Any]:
"""Get performance metrics for the orchestrator""" """Get performance metrics for the orchestrator"""
return { return {
@@ -6579,7 +6571,6 @@ class TradingOrchestrator:
except Exception as e: except Exception as e:
logger.error(f"Error adding decision fusion training sample: {e}") logger.error(f"Error adding decision fusion training sample: {e}")
def _train_decision_fusion_network(self): def _train_decision_fusion_network(self):
"""Train the decision fusion network on collected data""" """Train the decision fusion network on collected data"""
try: try:
@@ -8133,7 +8124,6 @@ class TradingOrchestrator:
except Exception as e: except Exception as e:
logger.error(f"Error initializing checkpoint manager: {e}") logger.error(f"Error initializing checkpoint manager: {e}")
self.checkpoint_manager = None self.checkpoint_manager = None
def autosave_models(self): def autosave_models(self):
"""Attempt to autosave best model checkpoints periodically.""" """Attempt to autosave best model checkpoints periodically."""
try: try:

26
web/clean_dashboard.py
View File

@@ -1280,13 +1280,15 @@ class CleanTradingDashboard:
@self.app.callback( @self.app.callback(
Output('price-chart', 'figure'), Output('price-chart', 'figure'),
[Input('interval-component', 'n_intervals')], [Input('interval-component', 'n_intervals'),
Input('show-pivots-switch', 'value')],
[State('price-chart', 'relayoutData')] [State('price-chart', 'relayoutData')]
) )
def update_price_chart(n, relayout_data): def update_price_chart(n, pivots_value, relayout_data):
"""Update price chart every second, persisting user zoom/pan""" """Update price chart every second, persisting user zoom/pan"""
try: try:
fig = self._create_price_chart('ETH/USDT') show_pivots = bool(pivots_value and 'enabled' in pivots_value)
fig = self._create_price_chart('ETH/USDT', show_pivots=show_pivots)
if relayout_data: if relayout_data:
if 'xaxis.range[0]' in relayout_data and 'xaxis.range[1]' in relayout_data: if 'xaxis.range[0]' in relayout_data and 'xaxis.range[1]' in relayout_data:
@@ -1301,6 +1303,15 @@ class CleanTradingDashboard:
xref="paper", yref="paper", xref="paper", yref="paper",
x=0.5, y=0.5, showarrow=False) x=0.5, y=0.5, showarrow=False)
# Display state label for pivots toggle
@self.app.callback(
Output('pivots-display', 'children'),
[Input('show-pivots-switch', 'value')]
)
def update_pivots_display(value):
enabled = bool(value and 'enabled' in value)
return "ON" if enabled else "OFF"
@self.app.callback( @self.app.callback(
Output('closed-trades-table', 'children'), Output('closed-trades-table', 'children'),
[Input('slow-interval-component', 'n_intervals')] # OPTIMIZED: Move to 10s interval [Input('slow-interval-component', 'n_intervals')] # OPTIMIZED: Move to 10s interval
@@ -1651,7 +1662,7 @@ class CleanTradingDashboard:
elif hasattr(panel, 'render'): elif hasattr(panel, 'render'):
panel_content = panel.render() panel_content = panel.render()
else: else:
panel_content = html.Div([html.Div("Training panel not available", className="text-muted small")]) panel_content = [html.Div("Training panel not available", className="text-muted small")]
logger.info("Successfully created training metrics panel") logger.info("Successfully created training metrics panel")
return panel_content return panel_content
@@ -1663,10 +1674,10 @@ class CleanTradingDashboard:
logger.error(f"Error updating training metrics with new panel: {e}") logger.error(f"Error updating training metrics with new panel: {e}")
import traceback import traceback
logger.error(f"Traceback: {traceback.format_exc()}") logger.error(f"Traceback: {traceback.format_exc()}")
return html.Div([ return [
html.P("Error loading training panel", className="text-danger small"), html.P("Error loading training panel", className="text-danger small"),
html.P(f"Details: {str(e)}", className="text-muted small") html.P(f"Details: {str(e)}", className="text-muted small")
], id="training-metrics") ]
# Universal model toggle callback using pattern matching # Universal model toggle callback using pattern matching
@self.app.callback( @self.app.callback(
@@ -2234,7 +2245,7 @@ class CleanTradingDashboard:
# Return None if absolutely nothing available # Return None if absolutely nothing available
return None return None
def _create_price_chart(self, symbol: str) -> go.Figure: def _create_price_chart(self, symbol: str, show_pivots: bool = True) -> go.Figure:
"""Create 1-minute main chart with 1-second mini chart - Updated every second""" """Create 1-minute main chart with 1-second mini chart - Updated every second"""
try: try:
# FIXED: Always get fresh data on startup to avoid gaps # FIXED: Always get fresh data on startup to avoid gaps
@@ -2404,6 +2415,7 @@ class CleanTradingDashboard:
self._add_trades_to_chart(fig, symbol, df_main, row=1) self._add_trades_to_chart(fig, symbol, df_main, row=1)
# ADD PIVOT POINTS TO MAIN CHART (overlay on 1m) # ADD PIVOT POINTS TO MAIN CHART (overlay on 1m)
if show_pivots:
try: try:
pivots_input = None pivots_input = None
if hasattr(self.data_provider, 'get_base_data_input'): if hasattr(self.data_provider, 'get_base_data_input'):

15
web/layout_manager.py
View File

@@ -226,6 +226,21 @@ class DashboardLayoutManager:
html.Hr(className="my-2"), html.Hr(className="my-2"),
# Leverage Control # Leverage Control
html.Div([
html.Label([
html.I(className="fas fa-compass me-1"),
"Show Pivot Points: ",
html.Span(id="pivots-display", children="ON", className="fw-bold text-success")
], className="form-label small mb-1"),
dcc.Checklist(
id='show-pivots-switch',
options=[{'label': '', 'value': 'enabled'}],
value=['enabled'],
className="form-check-input"
),
html.Small("Toggle pivot overlays on the chart", className="text-muted d-block")
], className="mb-2"),
html.Div([ html.Div([
html.Label([ html.Label([
html.I(className="fas fa-sliders-h me-1"), html.I(className="fas fa-sliders-h me-1"),

9
web/models_training_panel.py
View File

@@ -21,7 +21,7 @@ class ModelsTrainingPanel:
def __init__(self, orchestrator=None): def __init__(self, orchestrator=None):
self.orchestrator = orchestrator self.orchestrator = orchestrator
def create_panel(self) -> html.Div: def create_panel(self) -> Any:
try: try:
data = self._gather_data() data = self._gather_data()
@@ -34,12 +34,13 @@ class ModelsTrainingPanel:
if data.get("system_metrics"): if data.get("system_metrics"):
content.append(self._create_system_metrics_section(data["system_metrics"])) content.append(self._create_system_metrics_section(data["system_metrics"]))
return html.Div(content, id="training-metrics") # Return children (to be assigned to 'training-metrics' container)
return content
except Exception as e: except Exception as e:
logger.error(f"Error creating models training panel: {e}") logger.error(f"Error creating models training panel: {e}")
return html.Div([ return [
html.P(f"Error loading training panel: {str(e)}", className="text-danger small") html.P(f"Error loading training panel: {str(e)}", className="text-danger small")
], id="training-metrics") ]
def _gather_data(self) -> Dict[str, Any]: def _gather_data(self) -> Dict[str, Any]:
result: Dict[str, Any] = {"models": {}, "training_status": {}, "system_metrics": {}} result: Dict[str, Any] = {"models": {}, "training_status": {}, "system_metrics": {}}